Sharif Digital Repository

In this paper, we show that every 2-regular graph with three components of the form 2C4p∪ C4m has an αlabeling, except for the case p = m = 1. Furthermore, we present some general results for graphs composed of the disjoint union of cycles. The results considerably enlarge the class of 2-regular graphs known to have α-valuations  

In this paper, an algorithm based on ant colony optimization metaheuristic is proposed for finding solutions to the well-known graceful labeling problem of graphs. Despite the large number of papers published on the theory of this problem, there are few particular techniques introduced by researchers for gracefully labeling graphs. The proposed algorithm is applied to many classes of graphs, and the results obtained have proven satisfactory when compared to those of the existing methods in the literature  

A labeling (or valuation) of a graph G = (V, E) is a map that carries graph elements to numbers (usually to the positive or non-negative integers). The two famous labeling methods are called graceful labeling and edge-magic total labeling. A function f is called a graceful labeling of graph G with m edges if f is an injection from the vertices of G to the set {0, 1,...,m} such that when each edge (x, y) is assigned the label |f(x) - f(y)|, the resulting edge labels are distinct. An edge-magic total labeling is another well-known labeling that was motivated by the notion of magic squares in number theory. Despite the large number of papers published on the subject of graph labeling, there are... 

In this paper, an algorithm based on Ant Colony Optimization metaheuristic is proposed for finding solutions to the well-known graceful labeling problem of graphs. Despite the large number of papers published on the theory of this problem, there are few particular techniques introduced by researchers for gracefully labeling graphs. The proposed algorithm is applied to many classes of graphs, and the results obtained have proven satisfactory when compared to those of the existing methods in the literature. © 2007 IEEE  

In this paper, a diverse range of routing algorithms is classified into a new family of routings called zone-ordered label-based routing algorithms. The proposed classification is based on three common steps (factors) for generating such routings, namely, graph labeling, deadlock-free zones, and zone ordering. The main goal of this classification is to define several new routing concepts and streamline the knowledge on routing algorithms. Following the classification, a novel methodology is proposed to generate routing algorithms for irregular networks. The methodology uses the three mentioned steps to generate deadlock-free routings. Consequently, the methodology-based routings fall into... 

In this paper, we present a new unsupervised coreference resolution method, that models coreference resolution as a graph labeling problem. The proposed approach uses an incremental graph development method that hierarchically deploys coreference features from higher precision to lower ones. Then, a relaxation labeling method is used for solving the graph labeling problem  

In this paper, a diverse range of routing algorithms is classified into a new family of routings called zone-ordered label-based routing algorithms. The proposed classification is based on three common steps (factors) for generating such routings, namely, graph labeling, deadlock-free zones, and zone ordering. The main goal of this classification is to define several new routing concepts and streamline the knowledge on routing algorithms. Following the classification, a novel methodology is proposed to generate routing algorithms for irregular networks. The methodology uses the three mentioned steps to generate deadlock-free routings. Consequently, the methodology-based routings fall into... 

In this paper, we introduce a novel method for human activity recognition that benefits from the structure and sequential properties of the test data as well as the training data. In the training phase, we obtain a fraction of data labels at constant time intervals and use them in a semi-supervised graph-based method for recognizing the user's activities. We use label propagation on a k-nearest neighbor graph to calculate the probability of association of the unlabeled data to each class in this phase. Then we use these probabilities to train an HMM in a way that each of its hidden states corresponds to one class of activity. These probabilities are used to learn the transition probabilities...